Predictive clinical indicators of refractory Mycoplasma pneumoniae pneumonia in children: A retrospective cohort study

To determine the clinical indicators predictive of refractory Mycoplasma pneumoniae pneumonia (RMPP) in children and develop a robust predictive model to aid in early identification and management. A retrospective cohort study was conducted on 338 children diagnosed with RMPP out of a total of 1500 cases of Mycoplasma pneumoniae at a single tertiary hospital from May 2021 to November 2023. Clinical and demographic data analyzed included age, gender, parents’ educational level, household income, body mass index, allergic constitution, and laboratory findings such as white blood cell count, neutrophil and lymphocyte counts, platelet count, and levels of C-reactive protein (CRP), D-dimer, and procalcitonin. Univariate and multivariate logistic regression analyses were performed to identify significant predictors of RMPP, and a predictive model was developed. Among the RMPP cohort, 52.4% were female, with a mean age of 6.07 ± 2.78 years. Multivariate analysis identified several significant predictors of poor prognosis, including higher body mass index, longer duration of fever, elevated white blood cell count, neutrophil count, C-reactive protein levels, and increased neutrophil to lymphocyte ratio and platelet to lymphocyte ratio. The model demonstrated outstanding diagnostic performance, with an area under the receiver operating characteristic curve of 0.963 (95% confidence interval: 0.946–0.981). Our study identifies key clinical indicators with significant diagnostic accuracy for predicting RMPP in children. The predictive model established offers a valuable tool for clinicians, potentially improving RMPP outcomes through timely intervention.


Introduction
[15] Despite advances in diagnostic techniques, RMPP remains difficult to predict and manage effectively. [16]arly identification of children at risk for RMPP is critical.Several studies have highlighted the potential role of demographic factors, clinical features, and laboratory markers as prognostic indicators. [17,18]For instance, age and gender have been inconsistently associated with RMPP outcomes. [19]In contrast, laboratory markers such as elevated white blood cell count, neutrophil predominance, and increased inflammatory markers like C-reactive protein (CRP) have shown a more consistent correlation with the disease severity. [20]he development of a reliable predictive model for RMPP could improve the clinical approach to managing M pneumoniae infections.Predictive modeling may enable healthcare providers to identify children at high risk for poor outcomes and tailor treatment strategies accordingly. [10]Such a model could integrate various prognostic indicators, potentially improving the specificity and sensitivity of RMPP predictions.
This study aims to bridge the existing knowledge gap by analyzing a comprehensive set of variables to construct a predictive model with the potential to forecast RMPP with high accuracy.By determining the clinical utility of various indicators and constructing a robust model, we hope to contribute to better patient stratification and individualized patient care in pediatric RMPP.

Study design and population
This study analyzed a subset of 338 children diagnosed with RMPP from a larger cohort of 1500 patients treated for M pneumoniae pneumonia at a single tertiary hospital between May 2021 and November 2023.The inclusion criteria were as follows: (1) all patients met the diagnostic standards for M pneumoniae pneumonia as outlined in the eighth edition of "Zhufutang Practical Pediatrics"; (2) symptoms did not improve after more than 1 week of treatment; (3) pulmonary imaging indicated extensive consolidation areas with necrosis, and bronchoscopic examination revealed mucous plugs and mucosal necrosis.Exclusion criteria included: (1) severe tracheal/pulmonary maldevelopment or malformation; (2) congenital heart disease; (3) genetic metabolic diseases; (4) immunodeficiency disorders; (5) incomplete medical records.

Data collection and variables
Clinical data for this study were systematically extracted from the hospital's electronic health records.The collected information included demographic details (age, gender), which are fundamental for assessing any age and gender-related variations in treatment response.We also included socioeconomic factors such as parental education levels and household income brackets to explore potential correlations with health outcomes and healthcare accessibility.The clinical data encompassed a range of variables critical for diagnosing and monitoring the progression of M pneumoniae pneumonia.These included full blood counts and differential counts (white blood cell [WBC] count, neutrophil and lymphocyte counts, platelet count) to evaluate the immune response to infection.Key derived ratios such as the neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) were calculated to provide insights into the systemic inflammatory response, which can be significant in severe infections like RMPP.Additionally, specific biomarkers were selected based on their proven diagnostic and prognostic relevance in inflammatory and infectious processes.CRP was included as a marker of inflammation, D-Dimer (DD) for assessing coagulation abnormalities often seen in severe infections, procalcitonin (PCT) as a marker more specific to bacterial infections, and lactate dehydrogenase (LDH) to gauge cellular damage and turnover.
Each variable was chosen not only for its clinical relevance but also for its potential to provide a comprehensive understanding of the factors that influence the severity and outcome of RMPP.This selection aims to enable a robust analysis of these variables' impact on the prognosis of RMPP in children.

Outcome assessment
The primary outcome of this study was the prognosis of pneumonia, adjudicated as "good" or "poor" based on patient response to treatment.Prognosis was classified as "good" if patients demonstrated clinical recovery within 10 days of initiating appropriate antibiotic therapy.Recovery was defined by resolution of symptoms, improvement in radiological findings, and normalization of inflammatory markers.Conversely, prognosis was classified as "poor" if symptoms persisted or worsened, requiring additional therapeutic interventions beyond the initial treatment period.To ensure a consistent and unbiased evaluation, the assessment of each patient's response was conducted by a panel of experienced pediatric pulmonologists.This panel reviewed comprehensive clinical data, including medical records, laboratory results, and imaging studies.The panel convened regularly to discuss and reach a consensus on each case, thereby standardizing the adjudication process across the cohort.

Ethical review
This retrospective cohort study was conducted in compliance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.Ethical approval for this study was granted by the Ethics Committee of Hejiang People's Hospital, with the approval number 2023-003.Due to the retrospective nature of this analysis and the use of anonymized patient data, the requirement for informed consent was waived by the Ethics Committee.This decision was based on the consideration that the study did not involve any direct interaction with the patients or any impact on their care, ensuring that patient confidentiality and integrity were maintained throughout the study process.

Statistical analysis
The associations between clinical characteristics and pneumonia prognosis were examined through univariate and multivariate logistic regression analyses, with results presented as odds ratios and 95% confidence intervals (CIs).The performance of clinical parameters as predictors of prognosis was assessed using the Receiver Operating Characteristic (ROC) curve analysis.The Area Under the Curve (AUC) was calculated to quantify the predictive accuracy, and the Youden index was employed to identify optimal cutoff points for each parameter.The statistical threshold for significance was set at P < .05.All statistical analyses were performed using SPSS Statistics software, version 26 (IBM Corp., Armonk, NY).

Baseline characteristics of the study population
Table 1 presents the baseline characteristics of the study population, encompassing both clinical and demographic parameters.The study involved a total of 338 children diagnosed with refractory Mycoplasma pneumoniae pneumonia.The distribution of genders was nearly even, with 177 females (52.4%) and 161 males (47.6%).The average age of the participants was 6.07 years, with a standard deviation of 2.7762 years.In terms of educational background of the parents, 168 children (49.7%) had parents with a college degree or higher, while 170 children (50.3%) had parents with a high school education or below.Household income levels were reported as below 10,000 units for 179 children (53%) and above 10,000 units for 159 children (47%).The mean Body Mass Index (BMI) was 16.041 kg/ m² with a standard deviation of 2.0367.The study population showed a significant presence of allergic constitution, with 180 children (53.3%) reported as having allergies, and 158 children (46.7%) reported as not having allergies.The duration of fever averaged 5.1755 days, with a standard deviation of 1.0423 days.Laboratory findings revealed a mean WBC count of 6.5099 × 10 9 cells/L (standard deviation 1.5239), a mean neutrophil count of 4.08 × 10 9 cells/L (standard deviation 0.98257), and a mean lymphocyte count of 1.9409 × 10 9 cells/L (standard deviation 0.51149).The mean platelet count was 246.11 × 10 9 cells/L (standard deviation 51.978).The NLR and PLR were 2.5862 (standard deviation 0.50402) and 123.09 (standard deviation 20.9), respectively.CRP levels averaged 5.4267 mg/L (standard deviation 1.9855), DD averaged 0.41434 mg/L (standard deviation 0.057606), PCT levels averaged 0.21597 ng/ mL (standard deviation 0.054258), and LDH levels averaged 200.45 U/L (standard deviation 45.297).

Comparison of clinical characteristics between patients with good and poor prognosis
Table 2 provides a detailed comparison of demographic and clinical characteristics between 2 groups of patients: those with a good prognosis and those with a poor prognosis.The data include 241 patients in the good prognosis group and 97 in the poor prognosis group.For gender, the good prognosis group comprises 119 females (35.2%) and 122 males (36.1%), while the poor prognosis group includes 58 females (17.2%) and 39 males (11.5%), with a P-value of 0.083 suggesting no significant gender-based difference in prognosis.Age is reported as mean ± standard deviation, with the good prognosis group averaging 5.802 ± 2.8293 years and the poor prognosis group 6.7249 ± 2.5359 years; this difference is statistically significant (P-value = 0.006).Parental educational levels are categorized into "College or Above" and "High School or Below," with 123 (36.4%) parents from the good prognosis group and 45 (13.3%) from the poor group having attended college or above, yielding a P-value of 0.440.Household income is divided into 2 categories: below and above 10,000 units, with 132 (39.1%) in the lower income bracket in the good prognosis group compared to 47 (13.9%) in the poor prognosis group, and 109 (32.2%) versus 50 (14.8%) in the higher income bracket respectively, with a P-value of 0.292.The BMI is expressed as mean ± standard deviation, with the good prognosis group having a BMI of 15.731 ± 1.9732 kg/m², and the poor prognosis group with 16.812 ± 1.9967 kg/m², which is statistically significant (P-value < 0.001).For allergic constitution, 121 (35.8%) patients with good prognosis and 59 (17.5%) with poor prognosis report having an allergic constitution, with a P-value of 0.077.Clinical indicators such as duration of fever, WBC count, neutrophil count, lymphocyte count, platelet count, NLR, PLR, CRP, DD, PCT, and LDH are also reported.The duration of fever averages 4.9101 ± 0.98282 days in the good prognosis group and 5.8348 ± 0.88469 days in the poor prognosis group, significant at P-value < 0.001.Similarly, other laboratory values show significant differences, indicating varying levels of inflammation and other physiological responses between the groups.

Univariate and multivariate analysis of factors associated with disease prognosis
Table 3 offers a comprehensive analysis elucidating the associations between various demographic and clinical characteristics with the prognosis of the disease under study.The table includes data for 338 patients.For each characteristic, the Odds Ratios (OR) with 95% confidence intervals (CIs) and P values are provided, indicating the strength and direction of the associations.In the univariate analysis, gender is represented with males having an OR of 0.656 (95% CI: 0.407-1.058)compared to females, who serve as the reference category.The age of the patients is shown to influence prognosis significantly, with an OR of 1.130 (95% CI: 1.035-1.234)per year increase.Other variables such as parental educational level and household income are examined but do not show significant associations in univariate analysis.BMI shows a significant association, with an OR of 1.317 (95% CI: 1.161-1.493)per unit increase.For allergic constitution, those with allergies have an OR of 1.540 (95% CI: 0.953-2.487)compared to those without.Duration of fever significantly affects prognosis, with an OR of 2.963 (95% CI: 2.166-4.054)per day increase.Biological markers such as WBC, neutrophil count, lymphocyte count, and platelet count are also analyzed.Neutrophils and lymphocytes show strong associations with disease prognosis, with significant odds ratios in both analyses.Additionally, NLR, PLR, CRP, DD, and PCT are significant predictors in both univariate and multivariate analyses.Multivariate analysis adjusts for potential confounders and largely confirms the significance of many factors seen in univariate analysis.For instance, the duration of fever shows an even higher OR of 3.540 (95% CI: 2.313-5.419) in multivariate analysis.Similarly, biological markers such as CRP and PCT remain significant with ORs of 1.419 (95% CI: 1.184-1.701)and 1.817 (95% CI: 1.325-2.524),respectively.Forest plots are shown in Figure 1. ).Age, BMI (Body Mass Index), duration of fever, WBC (white blood cell count), neutrophil count, lymphocyte count, platelet count, NLR (neutrophil to lymphocyte ratio), PLR (platelet to lymphocyte ratio), CRP (C-reactive protein), DD (D-Dimer), PCT (procalcitonin), and LDH (lactate dehydrogenase) are expressed as mean ± standard deviation (SD).Units for variables are-age: years, BMI (Body Mass Index): kg/m², duration of fever: days, WBC (white blood cell count): ×10 9 cells/L, neutrophil count: ×10 9 cells/L, lymphocyte count: ×10 9 cells/L, platelet count: ×10 9  Pei and Luo • Medicine (2024) 103:34 Medicine effectiveness is quantified by its AUC, showcasing the ability to distinguish between children with and without refractory pneumonia.For example, the AUC for age is 0.597 with a CI of 0.532 to 0.662, and a cutoff value of 4.019 years, resulting in a specificity of 30.29% and a sensitivity of 88.66%.The parameter showing the highest diagnostic utility is the predictive model itself, with an AUC of 0.963 (CI: 0.946-0.981),a very high specificity of 82.988%, and a sensitivity of 96.907%.This model encapsulates the combined predictive capabilities of the included parameters.The ROC curve plot is shown in Figure 2.

Discussion
Our study contributes to the burgeoning field of predictive modeling for RMPP in children, a clinical challenge that has garnered increasing attention due to its complexity and the significant morbidity it can cause.2021) developed an early predictive scale for RMPP, identifying 6 prognostic indicators that closely align with our findings.Their scale, validated in both retrospective and prospective cohorts, demonstrates the feasibility and utility of early prediction in standard clinical practice. [21]Our model expands on this foundation by incorporating a broader array of variables, enhancing its predictive capacity and clinical applicability.
Similarly, the nomogram models developed by Cheng et al (2021) for predicting the risk of bronchitis obliterans [22] and by Zhang et al (2023) for plastic bronchitis underscore the significance of early, [23] accurate risk stratification in RMPP.These studies highlight specific clinical and laboratory markers, including WBC count and LDH levels, which emerged as significant predictors in our analysis as well.This convergence of findings underscores the critical role of inflammatory and immune response markers in the pathophysiology of RMPP and its severe complications.
Furthermore, the study by Cheng et al (2020) reinforces the importance of simplicity and usability in predictive tools for RMPP.Their nomogram, which also identified LDH and neutrophil ratio as significant predictors, exemplifies the potential of such models to facilitate early recognition and intervention in RMPP. [24]Our model mirrors this approach, emphasizing ease of use and clinical relevance to aid physicians in the timely management of at-risk children.
The collective insights from these studies, together with our own, highlight several key themes in the early prediction and management of RMPP.First, the consistent identification of specific predictors across different models attests to their fundamental role in the disease process and their potential utility in clinical practice.Second, the development and validation of these models underscore the feasibility of integrating predictive modeling into the routine care of children at risk for RMPP, potentially transforming the approach to this challenging clinical scenario.
In synthesizing these contributions, our study not only validates the significance of previously identified predictors but also introduces additional variables into the predictive framework, offering a more nuanced and comprehensive tool for clinicians.By doing so, we aim to enhance the precision of RMPP prognosis and, ultimately, to facilitate more targeted and effective interventions to mitigate the impact of this disease on affected children.
Additionally, our study acknowledges recent advancements in the identification of biomarkers for M pneumoniae infections.Notably, the research conducted by Papan et al (2023) introduces a combinatorial host-response biomarker signature (BV score) and its subanalytes TRAIL, IP-10, and CRP, which differentiate bacterial from viral etiologies in children with community-acquired pneumonia. [25]This study demonstrates the potential utility of these biomarkers in enhancing diagnostic precision.The BV score, in particular, provides a nuanced approach to understanding the host response, which is crucial in M pneumoniae due to its unique interplay between bacterial and viral infection characteristics.
In light of these findings, incorporating such biomarkers into future iterations of our predictive model could refine our ability to stratify risk and tailor interventions more effectively for RMPP.These newer markers, as detailed by Papan et al, may offer additional layers of diagnostic clarity and could be instrumental in addressing some of the limitations noted in our current approach, such as the need for external validation and the potential influence of unrecognized pathophysiological variations.The table presents the comparison of demographic and clinical characteristics between patients with a good and poor prognosis.Data are expressed as mean ± standard deviation (SD) for continuous variables and number and percentage (n [%]) for categorical variables.The P values indicate the level of statistical significance between the 2 groups.Units for variables are as follows: age: years, BMI (Body Mass Index): kg/m², duration of fever: days, WBC (white blood cell count): ×10 9 cells/L, neutrophil count: ×10 9 cells/L, lymphocyte count: ×10 9 cells/L, platelet count: ×10 9 cells/L, NLR (neutrophil to lymphocyte ratio): ratio, PLR (platelet to lymphocyte ratio): ratio, CRP (C-reactive protein): mg/L, DD (D-Dimer): mg/L, PCT (procalcitonin): ng/mL, LDH (lactate dehydrogenase): U/L.Gender, parents' educational level, household income, and allergic constitution are presented as number and percentage (n [%]).BMI = Body Mass Index, CRP = C-reactive protein, DD = D-Dimer, LDH = lactate dehydrogenase, NLR = neutrophil to lymphocyte ratio, PCT = procalcitonin, PLR = platelet to lymphocyte ratio, WBC = white blood cell count.
Our study, while offering valuable insights into predicting RMPP in children, is subject to several limitations that warrant consideration.Firstly, its retrospective nature may introduce selection bias, as the data were collected from past records, which could affect the generalizability of our findings.Secondly, being conducted at a single center, the study's results might not be directly applicable to other settings with different patient demographics or treatment protocols, thus limiting This table presents the results from both univariate and multivariate logistic regression analyses, examining the association of various demographic and clinical characteristics with disease prognosis.Odds ratios (OR) with 95% confidence intervals (CI) are reported to indicate the strength and direction of these associations, alongside their statistical significance (P value).
Reference categories are used for categorical variables to compute ORs.For gender, "Female" serves as the reference category.Similarly, for allergic constitution, "No" is considered the reference.Units for variables are as follows: Age: years, BMI (Body Mass Index): kg/m², duration of fever: days, WBC (white blood cell count): ×10 9 3 indicate statistically significant results, typically with a P-value <0.05.These values highlight key findings that are significantly different or noteworthy in the context of the analysis.In conclusion, our study complements and extends the existing literature on predictive modeling for RMPP in the pediatric population.By integrating a diverse array of clinical and laboratory markers, we have developed a comprehensive model that holds promise for the early identification of children at risk for severe outcomes.This model not only reflects the complexity of RMPP but also offers a pragmatic tool for clinicians to tailor interventions more effectively.Moving forward, the validation of our findings across broader cohorts and the integration of predictive models into clinical practice will be crucial steps toward mitigating the impact of this challenging condition on affected children.

Figure 1 .
Figure 1.Forest plot depicting the odds ratios (ORs) and 95% confidence intervals (CIs) for various clinical parameters associated with the prognosis of refractory Mycoplasma pneumoniae pneumonia in children.The horizontal lines represent the 95% CIs, the dots indicate the point estimate of the ORs, and the P values are provided to show the level of statistical significance.Parameters that have CIs not crossing the vertical line of no effect (OR = 1) are considered statistically significant.The plot illustrates the individual impact of each parameter, with age, BMI, duration of fever, WBC, neutrophil count, lymphocyte count, NLR, PLR, CRP, DD, and PCT included in the multivariate logistic regression model, guiding towards an informed prognosis assessment.BMI = Body Mass Index, CRP = C-reactive protein, DD = D-Dimer, NLR = neutrophil to lymphocyte ratio, PCT = procalcitonin, WBC = white blood cell count.
. The diagnostic accuracy is assessed using the area under the receiver operating characteristic (ROC) curve (AUC), with corresponding 95% confidence intervals (CI).Additionally, optimal cutoff values are determined alongside their specificity and sensitivity percentages.Units for variables are as follows: Age: years, BMI (Body Mass Index): kg/m², duration of fever: days, WBC (white blood cell count): ×10 9 cells/L, neutrophil count: ×10 9 cells/L, lymphocyte count: ×10 9 cells/L, platelet count: ×109 cells/L, NLR (neutrophil to lymphocyte ratio): ratio, PLR (platelet to lymphocyte ratio): ratio, CRP (C-reactive protein): mg/L, DD (D-Dimer): mg/L, PCT (procalcitonin): ng/mL.The "Model" row represents the combined predictive performance of the identified parameters within the constructed predictive model for refractory Mycoplasma pneumoniae pneumonia in children.Cutoff values are provided for each parameter, offering a threshold that optimizes the balance between specificity (true negative rate) and sensitivity (true positive rate) for the diagnosis of refractory Mycoplasma pneumoniae pneumonia.AUC = area under the curve, BMI = Body Mass Index, CI = confidence interval, CRP = C-reactive protein, DD = D-Dimer, NLR = neutrophil to lymphocyte ratio, PCT = procalcitonin, PLR = platelet to lymphocyte ratio, WBC = white blood cell count.

Figure 2 .
Figure 2. ROC curve illustrating the performance of the predictive model for refractory Mycoplasma pneumoniae pneumonia in children.The model shows an AUC (area under the curve) of 0.963, indicating an excellent diagnostic ability.The 95% confidence interval (CI) for the AUC ranges from 0.946 to 0.981, suggesting a high level of precision in the model's predictive power.The curve approaches the top-left corner of the graph, reflecting a high true positive rate (TPR) with a low false positive rate (FPR), which denotes high sensitivity and specificity of the model.ROC = receiver operating characteristic.
The authors have no funding and conflicts of interest to disclose.The datasets generated during and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.
a Department of Pharmacy, Hejiang People's Hospital, Luzhou City, Sichuan Province, China, b Department of Clinical Pharmacy, The Affiliated Hospital, Southwest Medical University, Luzhou City, Sichuan Province, China.

Table 4
assesses the diagnostic performance of various clinical parameters and a predictive model in identifying children with RMPP.The table provides the Area Under the ROC Curve (AUC), 95% CI, optimal cutoff values, and the corresponding specificity and sensitivity percentages for each parameter.Specific clinical parameters listed include age, BMI, duration of fever, WBC count, neutrophil count, lymphocyte count, NLR, PLR, CRP, DD, and PCT.Each parameter's diagnostic

Table 1
Baseline characteristics of the study population.
Gender, Parents' Educational Level, Household Income, and Allergic Constitution are presented as number and percentage (n[%]

Table 2
Comparison of clinical characteristics between patients with good and poor prognosis.

Table 3
Univariate and multivariate analysis of factors associated with disease prognosis.

Table 4
Diagnostic performance of clinical parameters and the predictive model in children with refractory Mycoplasma pneumoniae pneumonia.
This table evaluates the diagnostic performance of various clinical parameters and a predictive model for identifying children with refractory Mycoplasma pneumoniae pneumonia